Examining how unforeseen events affect accuracy and recovery of a non-linear autoregressive neural network in stock market prognoses

University essay from KTH/Skolan för datavetenskap och kommunikation (CSC); KTH/Skolan för datavetenskap och kommunikation (CSC)

Abstract: This report studies how a non-linear autoregressive neural network algorithm for stock market value prognoses is affected by unforeseen events. The study attempts to find out the recovery period for said algorithms after an event, and whether the magnitude of the event affects the recovery period. Tests of 1-day prognoses' deviations from the observed value are carried out on five real stock events and four created simulation sets which exclude the noisy data of the stock market and isolates different kinds of events. The study concludes that the magnitude has no discernible impact on recovery, and that a sudden event will allow recovery within days regardless of magnitude or change in price development rate. However, less sudden events will cause the recovery period to extend. Noise such as surrounding micro-events, aftershocks, or lingering instability of stock prices will affect accuracy and recovery time significantly.

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